Recent research has begun to question whether the evaluation of an AutoML system should be purely based on the generated models' predictive performance without considering interpretability [1]}, [2]}, [3]}, [4]}.
Interpreting AutoML systems can be categorized as (1) interpreting the resulting ML model on the underlying dataset, or (2) interpreting the HPO process itself.
In this paper, we focus on the latter.
